Abstract
To make the traditional applications benefit from multicore processors, the traditional Gaussian Elimination algorithm is improved to enhance its parallel performance under multicore architecture by matrix partition. The stability of the original algorithm is guaranteed. The hit rate of cache is improved by adjusting the computation sequence, the experiment shows that the speedup can reach 1.8 under duo core CPU environment when evaluating the inverse of dense matrix.
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© 2012 Springer-Verlag Berlin Heidelberg
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Mao, G., Zhang, X., Li, Y., Li, Y., Wei, L. (2012). Multicore-Based Performance Optimization for Dense Matrix Computation. In: Deng, W. (eds) Future Control and Automation. Lecture Notes in Electrical Engineering, vol 173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31003-4_12
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DOI: https://doi.org/10.1007/978-3-642-31003-4_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31002-7
Online ISBN: 978-3-642-31003-4
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